Cancer is a disease that emerges though genetic and epigenetic alterations that perturb molecular networks controlling cell growth, survival, and differentiation. To develop more targeted and efficacious cancer treatments, it is essential to situate and understand drug actions in this networked, systems-level context. For most anti-cancer drugs, only partial knowledge exists about their detailed mechanism of action. Even where targets have been defined, as with FDA-approved and in-clinical-trial drugs, broader off-target effects are often poorly understood. Compound activity and genomic profiling data over well-characterized cell line panels allows one to attempt computational prediction of molecular drug response determinants. However, these computational techniques exist in a continuum of complexity, and each has its assets and shortcomings. We have and will use a combination of approaches ranging from the simple to the complex for these purposes. We employ Pearson's or Spearman's, or Matthew's correlation-based approaches that can identify genomic features within cell line profiles that are significantly correlated with a compounds activity profile. This methodology has demonstrated the ability to recognize robustly correlated parameters. Pearson's correlation is employed in our CellMiner Pattern comparison, Cross correlation, and Genetic variant versus drug visualization, and utilize our Cell line signature and Genetic variant summation outputs. Our CellMinerCDB web-application provides multi-variant analysis using either linear regression or the LASSO machine learning approach. In addition, we use state-of-the-art mathematical techniques in our manuscripts to compare our large drug compound database to our extensive network of molecular factors using the NCI-60 cancer cell lines. Included are the elastic net regression algorithm (a machine learning approach) to identify robust, cumulative predictors of drug response (in progress). Included in these forms of analysis may be gene and microRNA transcript expression, gene copy number, gene sequence variation, transcript isoform status, and DNA methylation status. Pathway enrichment analysis for those identified molecular factors with significantly correlated molecular profiles may be applied. The selection of which analytical method to use to identify biologically-related events is not settled or simplistic. It is influenced by the biological question being asked, the level of biological knowledge available, the data types available, and the strengths, weaknesses, and applicability of each mathematical approach. It remains a field in its infancy. Among our previous successfully identified list of molecular-pharmacological associations are i) SLFN11 transcript expression for topoisomerase 1 and 2 inhibitors, alkylating agents, and DNA synthesis inhibitors (PMID: 22927417), ii) the identification of Ro5-3335 as a lead compound for Core Binding Factor leukemias (PMID: 22912405), iii) TP53 mutational status and the activity of the MDM2-TP53 interaction inhibitor nutlin iv) a multifactorial combination of ERBB1 and 2 expression and RAS-RAF-PTEN mutational status for the activity of erlotinib (PMID: 23856246), v) ATAD5 mutational status for the DNA-damaging drugs bleomycin, zorbamycin, and peplomycin (PMID: 25758781) vi) genetic variants for the DNA replication and repair gene MUS81 with the DNA synthesis inhibitor cladribine (PMID: 26048278), vii) genetic variants for the DNA damage repair gene RAD52 for the DNA damaging ifosfomide (PMID: 25032700), and CDK1 and 20 transcript isoforms for the CDK inhibitor palbociclib (PMID: 31113817).